Method and apparatus for spectrum sensing

ABSTRACT

In accordance with an example embodiment of the present invention the application discloses a method and an apparatus to receive a signal ( 501 ) in a radio device on a radio channel; calculate a plurality of cyclic autocorrelations ( 504′, 505 ) for the received signal ( 501 ), wherein calculating the plurality of cyclic autocorrelations comprises selecting a cyclic frequency ( 503 ) and at least two delay values ( 502, 512 ) corresponding to the plurality of cyclic autocorrelations ( 504′, 505 ); to compensate a phase offset of at least one of the plurality of cyclic autocorrelations ( 504 ′) by at least one compensation term ( 506 ) to obtain a plurality of phase compensated autocorrelations ( 504 ), wherein the compensation term ( 506 ) is dependent on the cyclic frequency ( 503 ) and a corresponding delay value of the at least two delay values; and to combine the plurality of phase compensated autocorrelations.

RELATED APPLICATION

This application was originally filed as PCT Application No.PCT/FI2011/050908 filed Oct. 19, 2011.

TECHNICAL FIELD

The present application relates generally to wireless communications andspectrum sensing in cognitive radio applications. More particularly thepresent application relates to detection of orthogonal frequencydivision multiplexing signals in cognitive radio devices.

BACKGROUND

Cognitive radios are devices, which are capable of adapting theirfunctionality according to the surrounding radio environment, userneeds, and/or other circumstances. For example, a cognitive radio devicemay receive request from a user to create communication link to anotheruser. The device may then independently determine how the communicationshould be actually implemented for optimized connectivity, capacity anduser experience. Cognitive radio systems generally employ dynamicspectrum use to achieve maximum flexibility. A cognitive radio device istherefore to perform spectrum sensing to determine whether certainfrequency range is currently used or not.

Orthogonal frequency division multiplexing (OFDM) is a multicarriermodulation method, which has been widely used in modern communicationsystems. Examples of such systems are the digital broadcasting standardsDAB (Digital Audio Broadcasting), DVB-T (Digital VideoBroadcasting-Terrestrial), DVB-T2 (Digital Video Broadcasting-2^(nd)generation terrestrial), DVB-H (Digital Video Broadcasting-Handheld),CMMB (Chinese Mobile Multimedia Broadcasting, ISDB-T (IntegratedServices Digital Broadcasting-Terrestrial), and T-DMB (TerrestrialDigital Multimedia Broadcasting). OFDM has been also applied in wirelesslocal and wide area networks (LAN/WAN) such as the IEEE 802.11standards. OFDM is also the technology for the latest cellularcommunication system in the 3GPP Long Term Evolution.

An OFDM signal may comprise a plurality of subcarriers modulated byconstellation symbols, i.e., complex numbers carrying the data. Themodulated subcarriers form individual subchannels which can be in atransmitter bundled together by the inverse fast Fourier transform(IFFT), which translates the created frequency domain subchannels into atime domain signal. Similarly, a receiver may perform fast Fouriertransform (FFT) to access the individual subchannels and to decode thetransmitted data.

Although the present invention is described using OFDM as an example, itshould not be interpreted as limiting the scope of the invention.Conversely, the present invention may be applied to any signals withsuitable statistical properties.

SUMMARY

Various aspects of examples of the invention are set out in the claims.

According to a first aspect of the present invention, a method comprisesreceiving a signal in a radio device on a radio channel; calculating aplurality of cyclic autocorrelations for the received signal, whereincalculating the plurality of cyclic autocorrelations comprises selectinga cyclic frequency and at least two delay values corresponding to theplurality of cyclic autocorrelations; compensating a phase offset of atleast one of the plurality of cyclic autocorrelations by at least onecompensation term to obtain a plurality of phase compensatedautocorrelations, wherein the compensation term is dependent on thecyclic frequency and a corresponding delay value of the at least twodelay values; and combining the plurality of phase compensatedautocorrelations.

According to a second aspect of the present invention, an apparatuscomprises: at least one processor; at least one memory containingexecutable instructions; and at least one shift register; wherein theexecutable instructions, when processed by the processor, cause at leastto: receive a signal on a radio channel; produce a plurality of delayedsignals from the received signal using the shift register; calculate aplurality of cyclic autocorrelations for the received signal using theplurality of delayed signals, wherein calculating the plurality ofcyclic autocorrelations comprises selecting a cyclic frequency and atleast two delay values corresponding to the plurality of cyclicautocorrelations; compensate a phase offset of at least one of theplurality of cyclic autocorrelations by at least one phase compensationterm to obtain a plurality of phase compensated autocorrelations,wherein the phase compensation term is dependent on the cyclic frequencyand the corresponding delay value; and combine the plurality of phasecompensated autocorrelations.

According to a third aspect of the present invention a computer programproduct comprises a computer-readable medium bearing computer programcode embodied therein for use with a computer, the computer program codecomprising: code for receiving a signal in a radio device on a radiochannel; code for calculating a plurality of cyclic autocorrelations forthe received signal, wherein calculating the plurality of cyclicautocorrelations comprises selecting a cyclic frequency and at least twodelay values corresponding to the plurality of cyclic autocorrelations;code for compensating a phase offset of at least one of the plurality ofcyclic autocorrelations by at least one compensation term to obtain aplurality of phase compensated autocorrelations, wherein thecompensation term is dependent on the cyclic frequency and thecorresponding delay value; and code for combining the plurality of phasecompensated autocorrelations

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of example embodiments of the presentinvention, reference is now made to the following descriptions taken inconnection with the accompanying drawings in which:

FIG. 1 presents an example of a spectrum sensing device;

FIG. 2 illustrates an exemplary OFDM signal in time domain;

FIG. 3 ab illustrates functionality of an exemplary dual-lag correlator;

FIG. 4 illustrates an example of calculating two autocorrelations inaccordance with an embodiment of the invention.

FIG. 5 presents an implementation of an algorithm according to anembodiment of the invention.

FIG. 6 presents an exemplary flow chart according to an embodiment ofthe invention.

FIG. 7 presents an exemplary flow chart according to an embodiment ofthe invention.

DETAILED DESCRIPTION OF THE FIGURES

A cognitive radio device may perform spectrum sensing for instance todetermine suitable frequency range for a transmission. The objective ofspectrum sensing is therefore to detect presence of ambientcommunication signals in certain frequency band. Preferably, thedetection should be both fast and reliable. In general, the detectionperformance is characterized by the detection sensitivity, i.e., thereceived signal power level that can still be detected with high enoughprobability in given detection time.

A short detection time is desirable for many reasons. For example, itmay provide better time-resolution, i.e. that the time instances whenthe channel state changes are observed with improved accuracy. It mayimprove primary user protection, because appearance of the primary usermay be detected with smaller delay and thus interfering secondarytransmissions may be ceased faster. A short detection time also leavesmore time for opportunistic access in a single-transceiver cognitiveradio system that alternates between sensing and channel access modes.More powerful algorithms may improve the detection sensitivity withoutincreasing the detection time. Using more complex algorithms may howeverlead to increased computational complexity, which translates into highernumber of logic gates and increased power consumption and cost in theactual implementation.

FIG. 1 presents an exemplary apparatus where one or more embodimentspresented herein may be implemented. Apparatus 100 may include at leastone processor 102 in connection with at least one memory 103 or othercomputer readable media. Memory 103 may be any type if informationstoring media including random access memory (RAM), read-only memory(ROM), programmable readable memory (PROM), erasable programmable memory(EPROM) and the like, and it may contain software in form of computerexecutable instructions.

Apparatus 100 may also comprise one or more radios, for example one ormore telecom radios 105, broadcast radios 106, or short-range radios 107such as Bluetooth radio or a wireless local area network (WLAN) radio.Apparatus 100 may further comprise a user interface 108, display 101,and audio input/output 108 for communicating with the user. Theapparatus may also comprise a battery 109 for delivering power forvarious operations performed in the device.

Apparatus 100 may use its resources, in particular the processor 102 andthe memory 103 for various purposes. For example, the device may tuneradio interfaces such as interfaces 105, 106, and 107 to certain radiochannels. A radio channel may comprise a range of frequencies on theelectromagnetic spectrum. Examples of the radio channels are thebroadcast channels on VHF (very high frequency) and UHF (ultra highfrequency) bands, cellular radio channels, and unlicensed radio channelsin the ISM (industrial, scientific, and medical) band.

Apparatus 100 may use the radio interfaces for spectrum sensing, whichmay comprise determining whether a particular radio channel is occupiedby radio transmissions from other apparatuses. The purpose of suchsearch may be to find empty radio channels, where the apparatus canperform radio communications without interfering the ongoing radiotransmissions and hence radio apparatuses. Spectrum sensing may alsoinclude scanning radio channels that do not have ongoing radiotransmissions and a thus receiving a radio signal may be understood toinclude receiving merely noise and/or interference.

Communication signals may be generally modeled as stochastic processeshaving certain statistical properties. A cyclostationary signal hascyclostationary properties, that is, the statistical properties of thesignal vary cyclically in time. An often used approximation for thisstrict definition of cyclostationarity is the definition of wide-sensecyclostationarity, where only the first and second order statistics,i.e., the mean and the autocorrelation of the signal are cyclic.

Cyclostationarity-based algorithms may be used in spectrum sensingimplementations, since they provide good detection sensitivity and aninherent ability to distinguish one signal type from another.Cyclostationarity based spectrum sensing algorithms rely on thecyclostationary properties of the signal to be detected, and hence theyare optimized for cyclostationary signals.

An OFDM signal may consist of a plurality of OFDM symbols. Each OFDMsymbol may include a cyclic prefix, i.e., a guard interval, which may beinserted in the beginning of each OFDM symbol. The cyclic prefixes aremainly inserted to eliminate intersymbol interference from the previousOFDM symbol, but they can be used also for other purposes such assynchronization. Alternatively, the cyclic prefix may be inserted at theend of the OFDM symbol. In further embodiments, modifications of suchcyclic extensions may be inserted both at the beginning and the end ofthe symbol, for example to provide a smooth change from OFDM symbol toanother.

FIG. 2 presents an exemplary OFDM signal 200 in time domain includingtwo OFDM symbols 201 and 211. OFDM symbol 201 consists of the data block203, which represents the plurality of subcarriers in time domain. Thelength of this portion is N_(FFT) which is also the FFT size of thesystem. As an example, an OFDM system may have 6817 active subcarrierswhich are modulated by QAM (Quadrature Amplitude Modulation)constellation symbols. The FFT size of the system may be 8192 (8K) andthe non-active subcarriers may set to zero before applying an 8192-pointIFFT in the transmitter. Applying IFFT to the subcarriers (active andnon-active) yields the time domain representation of the subcarriers,i.e., the data block 203.

The OFDM symbol 201 may also include a cyclic prefix (CP), which may bea replica of the last N_(CP) samples the data block 203. In someapplications the cyclic prefix may not be a direct copy of the last datablock sample, but it may be for example modified or combined to thesamples of the previous/next OFDM symbol. The length of the cyclicprefix is typically selected based on the characteristics of the radiochannel. An OFDM signal designed for a radio channel with long echoeswill typically have a long cyclic prefix to avoid interference betweenconsecutive OFDM symbols.

Insertion of a cyclic prefix causes an OFDM signal to havecyclostationary properties. Therefore, the cyclostationary basedspectrum sensing algorithms are suitable for detection of OFDM signalsdue to the strong periodic correlation provided by the cyclic prefixes.Detecting a cyclostationary signal may be generally done by calculatingthe conjugate cyclic autocorrelation

${{{\hat{R}}_{x}^{\alpha}(\tau)} = {\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}\;{{x\lbrack n\rbrack}{x^{*}\left\lbrack {n - \tau} \right\rbrack}{\mathbb{e}}^{{- {j2}}\;\pi\;\alpha\; n}}}}},$

where x*[n] is a complex valued input signal, α is a cyclic frequency,and τ is a delay parameter, which represents the delay of particularautocorrelation lag. The cyclic frequency α and the delay parameter τare related to the periodicity of the signal. Adjusting these parametersthe cyclostationarity based spectrum sensing algorithm may be tuned todetect different type of signals. Parameter N denotes the number ofreceived samples that are used for signal detection and thereforetogether with the signal sampling rate determines the detection time.

A hypothesis test may be applied to the correlation result to make adecision if x[n] exhibits cyclostationarity with certain α and τ valuesor not. The test statistic may be calculated as T=r_(x,K){circumflexover (Σ)}⁻¹r_(x,K) ^(T), where {circumflex over (Σ)}⁻¹ the estimate isthe estimate of inverse covariance matrix and vectorr_(x,K)=[{circumflex over (R)}_(x) ^(α)(τ₁) {circumflex over (R)}_(x)^(α)(τ₂) . . . {circumflex over (R)}_(x) ^(α)(τ_(K))] contains theestimates of the conjugate cyclic autocorrelation for K different delayvalues. When the received signal x[n] contains only additive whiteGaussian noise (AWGN), the test statistics is chi-square distributedwith 2K degrees of freedom. Therefore, a constant false alarm rate testcan be performed by comparing the test statistics to a threshold that isobtained from the inverse of the chi-square cumulative distributionfunction (cdf). If the observed test statistics value exceeds thepre-calculated threshold, then it is concluded that a signal isdetected.

Amplitude of the received signal samples x[n] can be normalized withoutaltering the cyclostationary characteristics of the received signal.This improves robustness against impulsive noise at the cost of onlyminor performance penalty in AWGN channel. This also leads to a simplerimplementation since noise statistics are known a priori and thereforethe covariance matrix does not need to be estimated from the receivedsamples. The input sample normalization may be denoted as spatial signfunction

${S\left( {x\lbrack n\rbrack} \right)} = \left\{ {\begin{matrix}\frac{x\lbrack n\rbrack}{{x\lbrack n\rbrack}} & {{x\lbrack n\rbrack} \neq 0} \\0 & {{x\lbrack n\rbrack} = 0}\end{matrix}.} \right.$

A spatial sign cyclic correlation estimator (SSCCE) may be defined as

${{{\hat{R}}_{S}^{\alpha}(\tau)} = {\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}\;{{S\left( {x\lbrack n\rbrack} \right)}{S\left( {x^{*}\left\lbrack {n - \tau} \right\rbrack} \right)}{\mathbb{e}}^{{- j}\; 2\;\pi\;\alpha\; n}}}}},$

where the spatial sign function has been used to normalize the inputsamples x[n] before calculating the correlation.

A constant alarm rate test similar to what is described above can bedefined as T_(s)=N∥r_(S,K)∥², where r_(S,K)=[{circumflex over (R)}_(S)^(α)(τ₁) {circumflex over (R)}_(S) ^(α)(τ₂) . . . {circumflex over(R)}_(S) ^(α)(τ_(K))]. For AWGN, the test statistics T_(S) is gammadistributed with shape factor K and scale factor 1. Consequently, thethreshold for constant false alarm rate test can be obtained frominverse of gamma cdf. The formulation of the constant alarm rate testshows the benefit of application of the spatial sign function as thetest statistics takes computationally much simpler form compared to theconventional test statistic T=r_(x,K){circumflex over (Σ)}⁻¹r_(x,K)^(T).

The general constant alarm rate test T_(S)=N∥r_(S,K)∥² utilizing Kcorrelation delay values may be simplified by limiting the number ofcorrelation delays. One example of such simplified test is a dual-lagcorrelator, which uses two correlation delays to calculate the teststatistics. A dual-lag correlator may be defined as

$T_{S,2} = {{N{\underset{\underset{C_{1}}{︸}}{\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}\;{{S\left( {x\lbrack n\rbrack} \right)}{S\left( {x^{*}\left\lbrack {n - \tau_{1}} \right\rbrack} \right)}{\mathbb{e}}^{{- j}\; 2\;\pi\;\alpha\; n}}}}}^{2}} + {N{\underset{\underset{C_{2}}{︸}}{\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}\;{{S\left( {x\lbrack n\rbrack} \right)}{S\left( {x^{*}\left\lbrack {n - \tau_{2}} \right\rbrack} \right)}{\mathbb{e}}^{{- j}\; 2\;\pi\;\alpha\; n}}}}}^{2}}}$

where τ₁ and τ₂ represent the two correlation delays and c₁ and c₂ arethe autocorrelations corresponding to these delays.

This dual-lag correlator may be used to detect OFDM signals by selectingthe two delays such that they match the periodicity of an OFDM signal.Suitable values for the delays are for example τ₁=N_(FFT) andτ₂=N_(FFT). In an alternative embodiment, the equation may be writtensuch that the system becomes causal.

In yet another embodiment, the cyclic frequency α may be selected tomatch the properties of an OFDM signal. In a further embodiment, thecyclic frequency may be defined by the FFT size and cyclic prefixlength, for example by α=1/(N_(CP)+N_(FFT)), where N_(CP) is the numberof samples in the cyclic prefix and N_(FFT) denotes the size of the IFFTthat is used to form the data part of the OFDM symbol. A particular lagof the autocorrelation is corresponds to the delay that is used whencalculating the autocorrelation. Both lags can be utilizedsimultaneously to increase the detection sensitivity while keeping thedetection time constant.

For this dual-lag mode for OFDM, the spatial sign cyclic correlationestimator can be written as

$T_{S,2} = {{N{\underset{\underset{C_{1}}{︸}}{\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}\;{{S\left( {x\lbrack n\rbrack} \right)}{S\left( {x^{*}\left\lbrack {n - N_{FFT}} \right\rbrack} \right)}{\mathbb{e}}^{{- j}\; 2\;\pi\;\alpha\; n}}}}}^{2}} + {N{\underset{\underset{C_{2}}{︸}}{\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}\;{{S\left( {x\lbrack n\rbrack} \right)}{S\left( {x^{*}\left\lbrack {n + N_{FFT}} \right\rbrack} \right)}{\mathbb{e}}^{{- j}\; 2\;\pi\;\alpha\; n}}}}}^{2}}}$

where the two conjugate cyclic autocorrelation functions, obtained usingdifferent delay values +N_(FFT) and −N_(FFT), are denoted as c₁ and c₂,respectively.

FIG. 3 a and FIG. 3 b illustrate functionality of an exemplary dual-lagcorrelator designed to detect an OFDM signal 300. The two delay valueshave been selected to be +N_(FFT) and −N_(FFT). In FIG. 3 a, a samplex[n] is located at the end of an OFDM symbol 301. Since the cyclicprefix is a replica of the end of the data block, sample x[n] has highcorrelation to sample x[n−N_(FFT)]. On the contrary, sample x[n] doesnot correlate to sample x[n+N_(FFT)], since the data block of thefollowing OFDM symbol 311 is independent of the data block of thecurrent OFDM symbol 301.

In FIG. 3 b, a sample x[n] is located in the beginning of the OFDMsymbol 311. Since the cyclic prefix is a replica of the end of the datablock, sample x[n] has high correlation to sample x[n+N_(FFT)]. On thecontrary, sample x[n] does not correlate to sample x[n−N_(FFT)], sincethe data block of the preceding OFDM symbol 301 is independent of thedata block of the current OFDM symbol 311.

From the example above it can be seen that the two autocorrelations c₁and c₂ produce correlation in different parts of the OFDM signal. Whencalculating the decision statistics, both autocorrelation functions c₁and c₂ should be taken into account. However, combining the twocorrelations is not directly possible. Instead, absolute square of bothconjugate cyclic autocorrelation functions c₁ and c₂ is calculated priorto the final sum.

FIG. 4 further illustrates the two autocorrelations. OFDM signals 401and 403 represent the differently delayed (±τ) versions of the OFDMsignal 402. It can be seen that the correlations from the two lags 404and 405 occur at different time periods and the relative delay betweenthe detected correlations is equal to the cyclic prefix length.

The formulation of the dual-lag correlator includes the exponent terme^(−2παn) in order to tune the algorithm to the correct cyclic frequencyaccording to the statistical properties of the signal to be detected.This term collects the desired cyclic frequency, which improves theperformance over conventional detectors without the exponent term. Sucha conventional detector is less complex, but it is not able to optimallyemploy the cyclostationary properties of the signal, which leads toinferior performance compared to the cyclic correlation. Also, using theexponent term makes the signal detection more robust to DC (directcurrent) offset. An object of the present invention is a low complexdetector, which utilizes the cyclostationary properties of the signal tobe detected.

The two autocorrelation functions c₁ and c₂ present two complex valuesthat have some magnitude and phase in complex plane. The stronger thecorrelation, the larger the magnitudes. Since the two autocorrelationfunctions produce correlation at different parts of the signal, thecyclic frequency of the dual-lag correlation causes a phase offsetbetween the two autocorrelation functions c₁ and c₂. Hence, directaddition of the two autocorrelations may cause degradation in detectionperformance.

The phase offset between the two autocorrelation functions is denoted asφ=arg(C₁)−arg(C₂). It turns out that when detecting an OFDM signal withdelay values +N_(FFT) and −N_(FFT), the phase difference □ is constantfor consecutive OFDM symbols and can be expressed as a function of thecorrelation delay, N_(FFT), and the cyclic frequency, α, byφ=2πN_(FFT)α. This can be in another embodiment presented as

$\phi = {2\pi{\frac{N_{FFT}}{N_{CP} + N_{FFT}}.}}$

The computational complexity of the dual-lag correlator may beadvantageously reduced by utilizing the constant characteristic of thephase offset. If the phase offset is compensated, the twoautocorrelations can be combined, which simplifies the implementationdual-lag correlator. Different embodiments of the combining may includedirect summation, maximum ratio combining, weighted average and thelike.

A phase compensated dual-lag correlator according to an embodiment ofthe invention may be defined as

$T_{{SC},2} = {2N{{\frac{1}{2N}{\sum\limits_{n = 0}^{N - 1}\;\left( {{{S\left( {x\lbrack n\rbrack} \right)}{S\left( {x^{*}\left\lbrack {n - \tau_{1}} \right\rbrack} \right)}{\mathbb{e}}^{{- j}\; 2\;\pi\;\alpha\; n}} + {{S\left( {x\lbrack n\rbrack} \right)}{S\left( {x^{*}\left\lbrack {n - \tau_{2}} \right\rbrack} \right)}{\mathbb{e}}^{{{- j}\; 2\;\pi\;\alpha\; n} + \phi}}} \right)}}}^{2}}$

where the phase of latter autocorrelation is compensated. Thecompensation term may depend on the characteristics of the signal to bedetected. In the example case of OFDM, the compensation term maycomprise the cyclic frequency, which may be calculated using the OFDMparameters N_(CP) and N_(FFT).

The main difference when compared to the non-phase compensated dual-lagcorrelator is the constant phase shift, φ, in the second exponent term.Computationally the phase compensated form is simpler because it doesthe summation of the two cyclic autocorrelation functions beforecalculating the absolute square value. In this case, the absolute squarevalue operation is executed only once. Test statistics for the phasecompensated dual-lag correlator is also gamma-distributed, but withscale factor 1 as opposed to scale factor 2 for the non-phasecompensated correlator. Therefore, this the phase compensated correlatorprovides improved detection sensitivity for the same amount of receivedsamples.

In another embodiment according to the invention, the phase compensateddual-lag correlation is calculated without the input samplenormalization S(x[n]) and the test statistics may be calculated

$T_{{SC},2} = {2N{{\frac{1}{2N}{\sum\limits_{n = 0}^{N - 1}\;\left( {{{x\lbrack n\rbrack}{x^{*}\left\lbrack {n - \tau_{1}} \right\rbrack}{\mathbb{e}}^{{- j}\; 2\;\pi\;\alpha\; n}} + {{x\lbrack n\rbrack}{x^{*}\left\lbrack {n - \tau_{2}} \right\rbrack}{\mathbb{e}}^{{{- j}\; 2\;\pi\;\alpha\; n} + \phi}}} \right)}}}^{2}}$

which may provide better performance since the information in theamplitudes of the received samples may not be lost.

Although the implementation of the algorithm is described using dual-lagcorrelation as an example, a person skilled in the art would understandthat the number of delays can be increased and that phase offsetcompensation can be applied also for multiple correlation lags.Similarly, it is to be understood that the calculations presented inthis document are merely examples and alternative versions orapproximations of the equations are within the scope of this invention.

The calculations may be further simplified by performing at least someof the calculations in angular domain. This helps to avoid complexmultiplications. Angular domain, i.e., polar coordinate representationsuits the algorithm well since the signal magnitude is one for allnon-zero samples. Amount of zero samples can be assumed to be assumednegligible. The phase of each sample may be denoted asφ_(x)[n]=arg(x[n]) and the phase compensated dual-lag correlator may bewritten as

${T_{{SC},2} = {2N{{\frac{1}{2N}{\sum\limits_{n = 0}^{N - 1}\;\left( {{\mathbb{e}}^{j{({{\varphi_{x}{\lbrack n\rbrack}} - {\varphi_{x}{\lbrack{n - \tau_{1}}\rbrack}} - {\varphi_{\alpha}{\lbrack n\rbrack}}})}} + {\mathbb{e}}^{j{({{\varphi_{x}{\lbrack n\rbrack}} - {\varphi_{x}{\lbrack{n - \tau_{2}}\rbrack}} - {\varphi_{\alpha}{\lbrack n\rbrack}} + \phi})}}} \right)}}}^{2}}},$

where the phase φ_(α)[n]=2πα/N.

Because additions are difficult to implement in angular domain, afterfinishing the calculation of the exponents the signal is mapped back toCartesian coordinates. The calculation of the argument and mapping backto Cartesian coordinates can be effectively implemented with thewell-known CORDIC algorithm.

FIG. 5 presents an exemplary implementation of the algorithm accordingto an embodiment of the invention. The structure calculates the phasecompensated dual-lag correlation using partly the angular domain tofurther reduce the complexity of the algorithm. The input signal x[n]501 is fed to a first type CORDIC block 511 to calculate the argument ofthe input samples so as to produce an angular domain received signal501′-1 (φ_(x)[n]). A random access memory (RAM) block 512 may be used toimplement the two delays. The shift register 512 may receive one or moredelay values 502 as input. This exemplary implementation is causal, sothe shift register 512 may produce differently delayed angular domainreceived signals 501′-2 (φ_(x)[n−τ]) and 501′-3 (φ_(x)[n−2τ]), where τand 2τ are used as delays instead of ±τ. A simple integrator 523comprising an adder 521 and a delay element 522 may be used toaccumulate the cyclic frequency α 503 to obtain the exponential cyclicfrequency signal 503′ (φ_(α)[n]) corresponding to the exponential term.Signal 503 may cause the algorithm to be selective to the target cyclicfrequency. Adders 515-1, 515-2, 515-3, 515-4, 515-5 may be then used tofinish the calculation of the exponential signals 504 (Φ_(x,+τ) ^(α)[n])and 505 (Φ_(x,−τ) ^(α)[n]) including correction of an exponential signal504′ by the phase offset 506 to obtain the phase compensated exponentialsignal 504.

After the exponential signals 504 and 505 have been resolved, they maybe mapped back to Cartesian coordinates using two second type CORDIC's514. The coordinate translations may be followed by two integrators,each integrator comprising a corresponding adder 531-1 or 531-2 and acorresponding delay element 532-1 or 532-2. Finally, the algorithm maybe completed by calculation of the absolute square comprising twodifferent multipliers 541-1 and 541-2 and an adder 542 which receivesthe signals from both multipliers 541-1 and 541-2, and a final divisionby divider 543 receiving the output from adder 542 and producing theoutput λ.

More particularly, signals φ_(x)[n−τ] and φ_(x)[n−2τ] output from theshift register 512 may be guided to adder 515-4. Signal φ_(x)[n−2τ] maybe inverted in order to subtract it from φ_(x)[n−τ] to produce inputsignal for adder 515-5. Similarly, signal φ_(x)[n−τ] and the angulardomain received signal φ_(x)[n], which may be output from the first typeCORDIC 511, may be guided to adder 515-1. Signal φ_(x)[n] may beinverted in order to subtract it from φ_(x)[n−τ] to produce input signalfor adder 515-2. Adder 515-2 may hence calculate the sum of the outputsignal of adder 515-1 and signal φ_(α)[n] from the integrator 523 toproduce a first angular cyclic autocorrelation signal 504′. Adder 515-5may similarly calculate the sum of the output signal of adder 515-4 andsignal φ_(α)[n] from the integrator 523 to produce a second angularcyclic autocorrelation signal 505. Adder 515-3 may be used to compensatethe phase offset by summing the phase offset correction term 506 tosignal 504′, hence producing a phase compensated angular autocorrelationsignal 504. In another embodiment the phase compensation can be doneafter adder 515-5 for signal 505.

Signals 504 and 505 may be input to second type CORDICs 514-1 and 514-2to convert the signals to Cartesian coordinate signals, each comprisingcomponents x and y. The x coordinates from the CORDICs 514-1 and 514-2may be guided to integrator 533-1 comprising adder 531-1 and delayelement 532-1 to accumulate the x signals from the CORDICs 514-1 and514-2. They coordinates from the CORDICs 514-1 and 514-2 may be guidedto integrator 533-2 comprising adder 531-2 and delay element 532-2 toaccumulate they signals from the CORDICs 514-1 and 514-2. Theaccumulated x coordinate signal from integrator 533-1 may be guided intothe two inputs of multiplier 541-1 to perform squaring of the xcoordinate signal. The accumulated y coordinate signal from integrator533-2 may be guided into the two inputs of multiplier 541-2 to performsquaring of they coordinate signal. The outputs from multipliers 541-1and 541-2 may be guided to adder 542 to combine the squared coordinatesignals. Output from the added 542 may be divided by 2N in block 543,which may take parameter N, the number of received sample used in thecorrelation, as an input. Finally, the algorithm may give the decisionstatistic as an output λ.

A control unit 550 may control the calculations providing reset signalfor integrators 523, 533-1 and 533-2. Control unit 550 may also providea signal for commanding the divider to perform divisions. The controlunit may receive as an input parameter the number of samples to be usedfor signal detection, N, which is then used to determine proper timingfor the resetting the integrators and/or calculating the division. Thecontrol unit may also provide an output signal λ_valid to indicate whenthe output of the detection algorithm is valid.

FIG. 6 presents an exemplary flow chart illustrating the functionalityof an exemplary spectrum sensing radio device. The spectrum sensing isstarted and first test statistics are calculated by the phasecompensated autocorrelations in block 601, e.g., using theimplementation presented in FIG. 5. The device may comprise controllogic to determine a threshold based on the characteristics of the radioenvironment, for example, the noise level. Alternatively, the thresholdmay be predetermined and fixed. The device may compare the calculatedtest statistics to the threshold in step 602, and determine whether thethreshold is exceeded. In response to detecting that the threshold isexceeded the device may conclude that a signal is found.

If the threshold is not exceeded, the receiver may continue signaldetection with the same parameters until a predefined time has lapsed.The receiver may also change the parameters of the phase compensatedautocorrelation to search for different type of signals. The receivermay also change the frequency on which the detection is performed. Ifstill no signal is found, the receiver may in step 605 determine thatthe frequency is empty. The receiver may then begin transmission on theempty frequency.

FIG. 7 presents an exemplary flow chart according to an embodiment ofthe invention. The exemplary algorithm presented in FIG. 7 may be forexample performed as part of block 601 in FIG. 6. In phase 701 a radiodevice receives a signal. In phase 702, the receiver may normalize thesamples of the received signal for example using the spatial signfunction described earlier in this document. In stem 702, the receivermay calculate a plurality of autocorrelations. For example, the receivermay calculate the spatial sign cyclic autocorrelation using two delayvalues as described earlier in this document. In phase 704, the receivermay compensate a phase offset of at least one of the calculatedautocorrelations. For example, the receiver may use the expectedproperties of the signal to be detected to determine how the phasecompensation should be performed. In step 705, the receiver may combinethe different autocorrelations and the receiver may also use thecombined autocorrelations to form a decision statistics. For example,the combining may be done using at least one of summation, maximum ratiocombining or weighted average. After phase 705 the algorithm may befinished and the receiver may use the decision statistics to determinewhether the received signal included signal(s) from other radio devices.It should be noted that the above phases of the algorithm are presentedfor merely exemplary purposes. A person skilled in the art wouldappreciate that the phases may be optional, and/or they may be combined,and/or performed as a part of another algorithms without departing fromthe spirit of the invention.

Apparatus 100 may be a cognitive radio device able to perform thecalculations of the presented correlations and phase compensation. Forexample the processor 102 may calculate the correlations, and/or performthe phase compensation based on the instructions stored in the memory,and/or normalize the input samples, and/or combining at least twoautocorrelations. In general, apparatus 100 may comprise means forperforming various types of calculations. For example, the apparatus maycomprise means for calculating the correlations, and/or means forperforming the phase compensation, and/or mean for normalizing the inputsamples, and/or means for combining at least two autocorrelations.

Apparatus 100 may include computer program code for causing to performthe various operations. For example, the apparatus may comprise computerprogram code to cause to perform calculations of the presentedautocorrelations, and/or perform the phase compensation based on theinstructions stored in the memory, and/or normalize the input samples,and/or combine at least two autocorrelations.

Without in any way limiting the scope, interpretation, or application ofthe claims appearing below, a technical effect of one or more of theexample embodiments disclosed herein is improving the detectionsensitivity of a cyclostationarity based spectrum sensing algorithm. Atthe same time the embodiments of the invention reduce the computationalcomplexity and hence enable faster calculation of the correlation. Thealgorithm provides also a shorter detection time and cheaperimplementation compared to conventional algorithms when targeting thesame detection performance.

Embodiments of the present invention may be implemented in software,hardware, application logic or a combination of software, hardware andapplication logic. The software, application logic and/or hardware mayreside on a cognitive radio device such as a mobile phone, laptop,handheld computer, portable music device, accessory or the like. Ingeneral, such device may include means for computing and means forstoring data on a readable memory in order to perform the functionalityaccording to one or more embodiments of the invention.

In an example embodiment, the application logic, software or aninstruction set is maintained on any one of various conventionalcomputer-readable media. In the context of this document, a“computer-readable medium” may be any non-transitory media or means thatcan contain, store, communicate, propagate or transport the instructionsfor use by or in connection with an instruction execution system,apparatus, or device, such as a computer, with one example of a computerdescribed and depicted in FIG. 1. A computer-readable medium maycomprise a computer-readable storage medium that may be anynon-transitory media or means that can contain or store the instructionsfor use by or in connection with an instruction execution system,apparatus, or device, such as a computer.

If desired, the different functions discussed herein may be performed ina different order and/or concurrently with each other. Furthermore, ifdesired, one or more of the above-described functions may be optional ormay be combined.

Although various aspects of the invention are set out in the independentclaims, other aspects of the invention comprise other combinations offeatures from the described embodiments and/or the dependent claims withthe features of the independent claims, and not solely the combinationsexplicitly set out in the claims.

It is also noted herein that while the above describes exampleembodiments of the invention, these descriptions should not be viewed ina limiting sense. Rather, there are several variations and modificationswhich may be made without departing from the scope of the presentinvention as defined in the appended claims.

What is claimed is:
 1. A method, comprising: receiving a signal in aradio device on a radio channel; calculating a plurality of cyclicautocorrelations for the received signal, wherein calculating theplurality of cyclic autocorrelations comprises selecting a cyclicfrequency and at least two delay values corresponding to the pluralityof cyclic autocorrelations; compensating a phase offset of at least oneof the plurality of cyclic autocorrelations by at least one compensationterm to obtain a plurality of phase compensated autocorrelations,wherein the compensation term is dependent on the cyclic frequency and acorresponding delay value of the at least two delay values; andcombining the plurality of phase compensated autocorrelations.
 2. Themethod of claim 1, wherein the received signal is an orthogonalfrequency division multiplexing signal, and wherein the cyclic frequencyand the delay values are selected based on a fast Fourier transform sizeand a cyclic prefix length of the orthogonal frequency multiplexingsignal.
 3. The method of claim 1, wherein calculating the plurality ofcyclic autocorrelations further comprises normalizing input samples ofthe received signal.
 4. The method of claim 1, wherein the combinedplurality of phase compensated autocorrelations forms decisionstatistics, further comprising: determining from the decision statisticswhether other radio devices are using the radio channel; and in responseto determining that other radio devices are not using the radio channel,using the radio channel for radio communication.
 5. The method of claim1, wherein samples of the received signal are transformed into angulardomain and the phase compensation is done by summing at least one phasecorrection term to at least one angular domain signal.
 6. An apparatus,comprising: at least one processor; at least one memory containingexecutable instructions; and at least one shift register; wherein theexecutable instructions, when processed by the processor, cause at leastto: receive a signal on a radio channel; produce a plurality of delayedsignals from the received signal using the shift register; calculate aplurality of cyclic autocorrelations for the received signal using theplurality of delayed signals, wherein calculating the plurality ofcyclic autocorrelations comprises selecting a cyclic frequency and atleast two delay values corresponding to the plurality of cyclicautocorrelations; compensate a phase offset of at least one of theplurality of cyclic autocorrelations by at least one phase compensationterm to obtain a plurality of phase compensated autocorrelations,wherein the phase compensation term is dependent on the cyclic frequencyand the corresponding delay value; and combine the plurality of phasecompensated autocorrelations.
 7. The apparatus of claim 6, furthercomprising: at least one first type Cordic block to convert the receivedsignal into an angular domain received signal causing the shift registerto produce a plurality of angular domain delayed signals; and at leastone adder to add the phase compensation term into at least one angularcyclic autocorrelation signal corresponding to the at least one of theplurality of cyclic autocorrelations to compensate the phase offset andto obtain a plurality of phase compensated angular cyclicautocorrelations corresponding to the plurality of phase compensatedautocorrelations.
 8. The apparatus of claim 7, further comprising: afirst integrator to produce an exponential cyclic frequency signal; anda plurality of adders to produce the at least one angular cyclicautocorrelation signal from the angular domain received signal, theplurality of angular domain delayed signals, and the exponential cyclicfrequency signal.
 9. The apparatus of claim 7, further comprising: aplurality of second type Cordic blocks to convert the plurality of phasecompensated angular cyclic autocorrelations into a plurality ofCartesian signals, each comprising a first and a second coordinatesignals; a plurality of second integrators to produce accumulated firstand second coordinate signals from the plurality of Cartesian signals; aplurality of multipliers to square the accumulated first and secondcoordinate signals to produce squared first and second coordinatesignals; and a divider to divide the squared first coordinate signal bythe squared second coordinate signal to produce an output signal. 10.The apparatus of claim 9, further comprising: a control unit to providea reset signal for the first integrator or at least one of the pluralityof the second integrators, a start signal for the divider, and anindication signal to indicate the validity of the output signal.
 11. Acomputer program product comprising a non-transitory computer-readablemedium bearing computer program code embodied therein for use with acomputer, the computer program code comprising: code for receiving asignal in a radio device on a radio channel; code for calculating aplurality of cyclic autocorrelations for the received signal, whereincalculating the plurality of cyclic autocorrelations comprises selectinga cyclic frequency and at least two delay values corresponding to theplurality of cyclic autocorrelations; code for compensating a phaseoffset of at least one of the plurality of cyclic autocorrelations by atleast one compensation term to obtain a plurality of phase compensatedautocorrelations, wherein the compensation term is dependent on thecyclic frequency and the corresponding delay value; and code forcombining the plurality of phase compensated autocorrelations.
 12. Thecomputer program product of claim 11, wherein the received signal is anorthogonal frequency division multiplexing signal, and wherein thecyclic frequency and the delay values are selected based on a fastFourier transform size and a cyclic prefix length of the orthogonalfrequency multiplexing signal.
 13. The computer program product of claim11, wherein the code for calculating the plurality of cyclicautocorrelations further comprises code for normalizing input samples ofthe received signal.
 14. The computer program product of claim 11,wherein the combined plurality of phase compensated autocorrelationsforms decision statistics, and wherein the computer program code furthercomprises: code for determining from the decision statistics whetherother radio devices are using the radio channel; and in response todetermining that other radio devices are not using the radio channel,code for using the radio channel for radio communication.
 15. Thecomputer program product of claim 11, wherein samples of the receivedsignal are transformed into angular domain and the phase compensation isdone by summing at least one phase correction term to at least oneangular domain signal.